Student Views in AI Ethics and Social Impact
Tudor-Dan Mihoc
a
, Manuela-Andreea Petrescu
b
and Emilia-Loredana Pop
c
a
Keywords:
Computer Science, AI, Ethics, Study, Students, Opinion, Threat, Benefit, Survey.
Abstract:
An investigation, from a gender perspective, of how students view the ethical implications and societal effects
of artificial intelligence is conducted, examining concepts that could have a big influence on how artificial
intelligence may be taught in the future. For this, we conducted a survey on a cohort of 230 second-year
computer science students to reveal their opinions. The results revealed that AI, from the student’s perspective,
will significantly impact daily life, particularly in areas such as medicine, education, or media. Men are
more aware of potential changes in Computer Science, autonomous driving, image and video processing,
and chatbot usage, while women mention more the impact on social media. Both men and women perceive
potential threats in the same manner, with men more aware of war, AI-controlled drones, terrain recognition,
and information war. Women seem to have a stronger tendency towards ethical considerations and helping
others.
1 INTRODUCTION
The exposure of AI in the media causes deep unrest in
society about the future of this technology. Suddenly,
more aware of AI tools (Vieweg, 2021), people be-
gin to debate their implications on the economy, the
job market, education, and, not least, ethical issues.
Researchers in (Ouchchy et al., 2020) examine and
categorize how these topics are portrayed in the press
to better understand how this representation could af-
fect public opinion. According to their findings, the
media coverage is still on the surface but has a realis-
tic and pragmatic perspective. Other topics related to
certain AI methods of information retrieval exposed
are: privacy, transparency, biases, censorship, filter
bubbles, security, accessibility, data handling, surveil-
lance, job displacement, or data ownership (Boren-
stein and Howard, 2021).
There is concern that these systems can reinforce
or magnify the biases present in the training data, re-
sulting in injustice and discrimination. Furthermore,
the processing of user data raises privacy problems
because it can lead to data breaches, illegal access,
and surveillance (Banciu and C
ˆ
ırnu, 2022).
Students, as future workers and decision makers,
should have a high level of understanding of AI ethics,
a
https://orcid.org/0000-0003-2693-1148
b
https://orcid.org/0000-0002-9537-1466
c
https://orcid.org/0000-0002-4737-4080
which will help them design and implement AI sys-
tems ethically. Therefore, an insight into students’
awareness of ethical concerns related to AI is very im-
portant.
In the study (Cernadas and Calvo-Iglesias, 2020)
the author acknowledges the existence of gender dis-
criminatory biases in the development of students. In
order to limit these, he underlines the need to intro-
duce gender perspectives in studies.
Such disparities can also be found in other coun-
tries. In some of Romania’s largest universities, ap-
proximately 31% of computer science students are
female. We asked related data to the number of
women/men that graduated computer science using a
law of transparency that states that public institutions
must provide any public information.
There have been studies (Pop and Coroiu, 2024;
Pop, 2024) on the inclination of middle school stu-
dents to technical education education using machine
learning, but little research has been conducted on the
students’ inclination to actually use and perfect AI.
A survey could reveal the knowledge gaps and
capture the diverse perspectives on this topic with re-
spect to gender. The purpose of the study was to
contribute to academic research on AI ethics and can
provide empirical evidence for scholarly publications,
furthering the understanding of ethical considerations
in AI.
In order to address these topics, we defined the
research questions as:
26
Mihoc, T.-D., Petrescu, M.-A. and Pop, E.-L.
Student Views in AI Ethics and Social Impact.
DOI: 10.5220/0013139500003932
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 17th International Conference on Computer Supported Education (CSEDU 2025) - Volume 1, pages 26-35
ISBN: 978-989-758-746-7; ISSN: 2184-5026
Proceedings Copyright © 2025 by SCITEPRESS – Science and Technology Publications, Lda.

⋆ Which domains will be most affected by AI?
gender-based perspective.
⋆ What are the ethical considerations related to the
potential threats associated with Artificial Intelli-
gence?
⋆ Who is willing to sacrifice ethical values for
money and social status? Is there a difference be-
tween how women and men perceive them?
We run a survey among students studying computer
science in their second year at Babes-Bolyai Univer-
sity, Romania, to get their opinions. We highlight
that at the end of the second term, the students have
a fundamental understanding of artificial intelligence.
They were knowledgeable with rule-based systems,
machine learning, decision trees, artificial neural net-
works, deep learning, intelligent systems, support
vector machines, clustering, and problem solving as
a search. Throughout the semester, a lecture focused
on fraud prevention and AI-related ethical issues. We
highlighted sensitive topics and approaches from the
perspective of an IT expert, using examples of fake
news and the techniques used to create them, as well
as how an AI could be tainted and biased through the
training database. The survey questions were related
to the lecture topics, allowing us to assess students’
understanding of the material as well as their thoughts
on the ethical implications of AI.
2 LITERATURE REVIEW
Gender disparities related to IT and, more recently,
artificial intelligence have been the subject of several
studies that have surfaced over the years.
The impact of artificial intelligence on female
employment is multifaceted. Men and women are
disproportionately affected by industrial growth and
platformization, driving the latter out of the labor
force (Mohla et al., 2021). A possible reason for per-
petuating stereotypes and discrimination is the dimin-
ished importance of women in AI development and
implementation. The need to address the gender gap
before it becomes pervasive and embedded in AI cul-
ture was highlighted by (Roopaei et al., 2021).
According to (Abdullah, 2019), AI is seen as
a complex potential threat that could affect human
behavior, replace jobs, and generate economic in-
equality. Cultural differences, discrimination, and in-
discriminate use of computing resources are just a
few ethical challenges that exacerbate these threats
(Baeza-Yates, 2022). In (Fisher and Fisher, 2023) the
authors call for increased cooperation and considera-
tion of human interests in the development of artificial
intelligence. They pleaded for legislative approaches
that address these concerns.
More ethical concerns about AI, such as bias, un-
employment, and socioeconomic inequality, are also
raised by (Green, 2018), who also emphasizes the
need for a more thorough analysis of these problems.
There are differences in the ethical decision mod-
els used by men and women (Schminke and Am-
brose, 1997), with a significant difference in the way
men and women perceive and prioritize ethical values
in different scenarios. When financial gain and so-
cial status are involved, research consistently shows
that women are more averse to ethical compromises
(Kennedy et al., 2013; Kennedy et al., 2014) than
men. They tend to associate business with immorality
more than men, and this aversion seems to be related
to their lower representation in high-ranking business
positions.
While the underrepresentation of women and mi-
norities in the IT workforce is a problem, their inclu-
sion may offer a way to address the industry’s skills
gap (Gallivan et al., 2006).
Particularly in AI jobs, there are large variations
between different groups. (Jakesch et al., 2022) dis-
covered that AI practitioners had different values in
mind than the broader public, with black and female
respondents giving ethical AI ideals more weight.
This aligns with the findings of (Callas, 1992), who
indicated that female workers were more inclined to
consider discriminatory actions to be morally wrong.
According to (Rothenberger et al., 2019; Zhou et al.,
2020), ethical standards for AI are crucial and may
reflect the values that black and female respondents
found most important.
(Cave et al., 2023; Brown et al., 2022; Yarger
et al., 2020) also found similar findings, demonstrat-
ing that minorities and women are notably underrep-
resented in AI development jobs. One major prob-
lem with AI teams is the attrition of individuals with
marginalized identities; many people leave because of
the culture and environment of these teams. This is-
sue is made worse by algorithmic bias in talent acqui-
sition tools, which keeps hiring practices unfair.
These studies collectively suggest that AI’s influ-
ence on gender is multifaceted, with the potential to
exacerbate existing inequalities.
With this research, we want to have a better under-
standing of these issues in relation to gender. By ad-
dressing the research questions, we want to determine
the students’ areas of interest in ethical concerns.
Student Views in AI Ethics and Social Impact
27

Table 1: Survey Questions.
Q1 Please specify your gender. Choice of male, female, or I prefer not to answer.
Q2 In which domains do you think Artificial Intelligence will have the greatest impact
and why? (Mention at least 3)
Q3 Which are considered the main benefits related to Artificial Intelligence that
might appear in the next 3 years?
Q4 Which are considered the main risks related to Artificial Intelligence that
might appear in the next 3 years?
Q5 Which are the main reasons you do NOT like a career in Artificial
Intelligence?
Q6 Which are the main reasons you would like to have a career in
Artificial Intelligence?
3 STUDY DESIGN
We used the guidelines provided by (Runeson and
H
¨
ost, 2009) to organize this research in accordance
with the norms of the scientific community, as stated
in (Ralph, 2021). We first determined the study’s
scope before deciding on the methodology.
Scope: The purpose of the study was to evaluate
students’ attitudes about the advancement of artificial
intelligence, as well as their interest in learning more
about and working in this field with respect to gender
identity.
Who: Second-year students from computer sci-
ence departments who took an introductory AI course
constituted the group of participants.
When: At the conclusion of the semester, we
asked the students to take a survey to find out their
perspectives.
How to: We used a hybrid strategy, analyzing the
gathered replies both quantitatively and qualitatively.
Observations: Analysing the answers, we found
out that students have concerns about the ethics of AI,
so we performed an analysis to find out AI related
ethics interests.
Participation: Participation in the study was vol-
untary, and the survey was anonymous, so we could
not map participants with their answers.
3.1 Survey Design
After we established the purpose of the investiga-
tion and formulated the research questions, we pre-
pared the survey questions. The process was an iter-
ative one; first, we elaborated on a set of questions,
then two authors discussed, proposed, and validated
changes to the form and structure of the questions.
The second draft was discussed with the third author,
and we agreed on the final versions of the questions.
We decided to add questions that we will not use in
the study, for example, questions Q3 and Q6. The
purpose of these questions was to prevent bias in the
received responses and to ask for positive and nega-
tive aspects.
We decided to use both closed and open questions.
The first question was used to determine the groups
and categories of participants (men versus women).
The other questions were used to encourage students
to freely express their opinions, since open-ended
questions can offer valuable insights into the students’
perspectives and perceptions. The questions asked in
the survey are listed in Table 1.
3.2 Participants
The target audience for our survey was formed by
second-year computer science students. As a result,
the set of participants consisted of 230 individuals, of
whom 198 agreed to participate in the study. Seven
of them did not wish to disclose their gender, 119 of
them were men and 72 women. Given the size of the
sample and the fact that the ratio of female partici-
pants is similar to the ratio of female students enrolled
in the faculty, we may conclude that the study’s fe-
male representation is statistically significant.
3.3 Methodology
The following methodologies used in this study are
consistent with those used in previous research efforts
(Petrescu and Motogna, 2023; Motogna. et al., 2021).
At the end of the second semester, we conducted
a survey that included open and closed-ended items.
Accountable questions facilitate the work with and in-
terpretation of the data, whereas open questions lead
to a greater degree of comprehension. The analy-
sis and interpretation of the responses to open ques-
tions were carried out using quantitative approaches.
The questionnaire surveys met the accepted empirical
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Table 2: Key item classes percentages for Data Processing.
Data Processing Data Processing Investing Economics Marketing
Men 7.81% 7.03% 5.47% 6.25%
Women 7.69% 8.97% 5.13% 5.13%
community norms (Ralph, 2021). Thematic analysis
was used for text interpretation, following the guide-
lines provided in (Braun et al., 2019). We applied the-
matic analysis using the following subsequent stages:
1. Two researchers independently attempted to
identify codes or key items within the text.
2. These key items were divided into classes ac-
cording to the frequently seen themes or cate-
gories. Those with low appearance frequency
will be reassigned to larger classes using tech-
niques such as generalization, elimination, and
reassignment.
3. The last step was a comprehensive debate
among all authors, so a certain degree of con-
fidence is achieved in the methodology. In the
discussions, reviews of several topics, represen-
tations, and supporting data for the categoriza-
tion procedure were included.
We computed the frequency of the important
terms. Even if some responses from students were
extremely succinct, many others included up to five
phrases or justifications. As a result, a response can
contain more items or important keys. A direct con-
sequence is that, in our analyses, the total cumulative
percentages will exceed 100%.
3.4 Data Collection
We proposed the questions set in English, as their line
of study is English based. The purpose of this choice
was to minimize the possible threats resulting from
translation. The information from the responses was
collected exactly as we received it, without any ma-
nipulation.
The students received a link to the anonymous sur-
vey via their MSTeams faculty account and were pro-
vided with enough time to ensure that their comments
were not too brief or non-existent.
4 RESULTS
In this section, we analyze the data collected and out-
line the results of student surveys. We took into ac-
count the different gender perspectives when we elab-
orated our analysis. We received 72 responses from
students who identified themselves as women, and we
considered it a representative number to have valid re-
sults. When we calculated the results, we computed
the prevalence of key items in women’s responses to
the total number of women, not the total number of
participants in the study.
When we asked students to mention at least three
domains (Q1), we had a couple of responses that men-
tioned four domains, there were three responses that
did not mention any domain, all the other students
mentioned two or three areas, some of them proving
details and explanations. We selected the key items,
classified and summarized them, and computed the
appearance percentages. Due to this process, the sum
of the percentages exceeds 100%. Responses to the
other survey questions were treated in the same way,
as each answer could contain zero, one, or more key
items, so the total percentages of prevalence of key
elements exceed 100%.
4.1 Q1: Which Domains Will Be Most
Affected by AI? Gender Based
Perspective?
To find the answer to this question, we used the an-
swers provided by the students to the survey question:
”In which domains do you think Artificial Intelligence
will have the greatest impact and why? (Mention at
least 3)”.
In the perception of the students, the domains with
the greatest impact that scored more than 10% of
their responses were medicine, education, program-
ming/computer science (CS), industries, and jobs that
involve repetitive tasks and autonomous driving. For
the domains most affected, there is no significant dif-
ference between the perceptions of men and women,
as can be seen in Figure 1, only three participants did
not answer this question.
Medicine was the domain most mentioned, scor-
ing above 50% for both men and women: ”health
care (detecting diseases, finding the best treatment,
etc.)”, ”I think AI will have the greatest impact in
medicine, in recognizing flaws unseen by the human
eye & in predictions of many kinds”. A larger differ-
ence appeared for the programming/computer sci-
ence domain, considered to be impacted by 30.47%
men and 21.79% women, ”Programming - helping
developers with repetitive code”, ”write code eas-
ier”, ”find errors such as missing coma”, ”in test-
Student Views in AI Ethics and Social Impact
29

Table 3: Key item classes percentages for Automatization.
AM Engineering Robots Research Physics Architecture Astronomy
Men 3.13% 3.91% 1.56% 1.56% 3.91% 2.34%
Women 7.69% 3.85% 2.56% 2.56% 3.85% 0.00%
Figure 1: Domains affected by AI. Gender Perspective.
ing”. A larger difference, more than 10% appears
to be related to autonomous driving, men seem to
be more interested in AI’s influence, they specify this
domain in a percent of 21.88% compared to women
10.26%: ”In the automotive industry, with regard to
cars that drive themselves”, ”self driving cars”. Ed-
ucation is mentioned more by women, but the differ-
ence is smaller compared to previous domains; only
about 4.55% women mention education more com-
pared to men: ”education as virtual tutoring will be
new thing”, ”Learning: you can access all knowledge
with AI”.
The rest of the domain were less prevalent in the
student’s answers, obtaining less than 10% and we
group them by categories based on the main charac-
teristics: automatization, the capacity to process large
amounts of data, and the capacity to generate content.
Large Data Processing Capabilities. In their opin-
ion, with the ability to process large amounts of data,
AI can have an impact in many areas, from analyz-
ing market trends and statistics to making predictions
for the stock market and betting: ”Identifying mar-
ket trends and strategies (economy)”, ”Data analysis,
works easily with large data”, ”Stock market predic-
tion, obviously”, ”Betting (sports, betting)”. Also,
concerns about marketing appear: ”because it is eas-
ier to detect patterns” in people’s behavior. A partic-
ular response synthesized this aspect: ”I think it will
have the greatest impact on education, data process-
ing, and research because it has access to information
beyond a single person’s capacity”. As shown in Ta-
ble 2, both genders fairly mentioned these aspects.
Automatization. (AM). An answer stated that ”Au-
tomatization of everyday tasks, can be done better
by AI than by humans”, either if we are referring to
”Surgery domain - great robots for operations” or to
”repetitive and boring tasks”. There are no signifi-
cant gender differences in perceptions, as can be seen
in Table 3.
Content Generation. A specific characteristic of
some AI tools is their ability to generate new con-
tent in terms of art, music, photo, and video editing
and processing. In art ”because it can generate high
quality free photos” or ”generate an image based on
description”, in ”design by generating images, pat-
terns or logos”, in fact ”In the creative industry (im-
ages, videos, music, etc.), the linguistic/news industry
(articles, translations, etc.)”. Music and even fash-
ion were mentioned: ”music: you can create music
with AI”, ”fashion: you can use different AI to create
clothes”. Men mention chatbots more compared to
women: ” Internet bots - easy to write text that looks
written by a human”, women mentioned more media
and social media: ”in algorithms used by social me-
dia platforms and in the video game industry because
it could learn about human behavior”.
Ethics Related Concerns. Some responses revealed
concerns related to mass manipulation through the de-
tection and use of patterns in people’s behavior. In
the view of the students, AI poses threats through ad-
vanced processing and generation of multimedia con-
tent (video, image, or audio) – ”deep fakes, it is a ma-
jor problem if they become hard to detect”. The use
of artificial intelligence in communication and social
media can have an impact on governments, wars, and
politics: ”politics because the generation of different
fake videos leads to disinformation”. Male partici-
pants also mentioned the use of drones, war recogni-
tion, and fighting. Although the question had a differ-
ent scope, there were responses concerned about pos-
sible job replacements since automation could elimi-
nate repetitive jobs: ”IT, in testing; I think it will be
the first job that will disappear”, ”industrial implies
that robots could replace people”.
Q1 Conclusion. The domains mentioned in the re-
sponses were diverse, suggesting that AI will have a
non-negligible impact on daily life. It was interesting
to analyze the fact that the domains most present in
the answers were the domains where there was a lot of
progress in AI: medicine, education, or photo/video
processing and generating.
In a general view, there are no significant differ-
ences between men’s and women’s perceptions re-
lated to the most affected domains; however, men
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30

Table 4: Key item classes percentages for Content Generation.
Creation Art Text Video/photo (Social) Chatbots
generation editing Media
Men 6.25% 4.69% 8.59% 4.69% 10.94%
Women 5.13% 1.28% 3.85% 7.69% 2.56%
seem more aware of the potential changes in Com-
puter Science, autonomous driving, image and video
processing, and chatbot usage (either to replace jobs
or to be used in personalized learning). Women men-
tion, compared to men, only the impact on social me-
dia.
4.2 Q2: What Are the Ethical
Considerations Related to the
Potential Threats Associated with
Artificial Intelligence?
Following the analysis of the responses received,
we classified the key items into classes, resulting in
five main classes of threats: economic, information-
related, general, apocalyptic fear, and absence of
risks. Additionally, there were students who did not
respond to this question or indicated that they were
indifferent to it. A set of responses, 5.24% of them,
emphasized the need for regulations in the field of ar-
tificial intelligence. A rather significant percentage of
23.03% of students did not answer to this question
and stated that they consider AI not to pose any risks
2.45% and other 1.47% mentioned that they don’t
care. We designated this question as optional to avoid
introducing bias, as we did not want to force students
to respond if they had not considered the implications
of AI. The main threats identified by us were related
to economic issues, misinformation and fake news,
ethical concerns, the military, the loss of human abil-
ities, and becoming uncontrolled. The gender-based
concerns are presented in Figure 2.
Figure 2: Major threats due to AI. Gender Perspective.
Economic Threats. are represented by a major cat-
egory: job cuts due to AI 26.56% men and 34.25%
women, the possible replacement of the artist’s work,
which was mentioned separately in 4.69% men’s re-
sponses and 1.37% women’s responses. Job cuts are
exemplified in responses such as: ”loss of jobs due
to human replacement by robots”, ”Job loss maybe
in programming” and even in creative domains such
as art: ”replacing artists” work or ”destruction of
multiple career paths (arts, writing currently endan-
gered)”. We got some responses that take into con-
sideration even ”market manipulation”, but in terms
of prevalence, responses that mention other domains
are rare.
Misinformation. Misinformation is the second most
mentioned threat, with a total of 10.16% men and
4.11% women. Separately, the threat of fake news
was mentioned by 10.94% of men and 6.85% of
women. AI can facilitate ”fake news spreading”, also
”misinformation will be more easily achieved”, and
”deep fake that becomes more and more convincing”.
Ethical Concerns. related to AI are mentioned as
ethical implications by 5.48% women and 8.59%
men, concerns related to copyright and/or plagiarism,
and a generic ”bad influence or ”bad direction” men-
tioned by 6.85% women and 5.47% men.
Ethical implications are mentioned in a general
manner, often quite succinct: ”the problem of ethic”,
”ethics concerns”, or ”I think there will be le-
gal and moral implications”, others offer more de-
tails: there is ”one enormous risk is that AI will be
needed for unethical or malicious purposes”. Pla-
giarism is mentioned as ”copyright of train data,
defamation, ethical concerns”, ”Art plagiarism, de-
commissioning artists, intellectual property theft (in-
tentional or not)”.
Military. The use of AI for military purposes ap-
pears in more than 14% of the answers, mentioned
by men and women in relatively similar percentages:
14.84% men versus 10.96% women: ”Use in mili-
tary, become smarter than humans, become incontrol-
lable”, ”Military: can lead to mass destruction”, can
be used for :”political biases” and ”propaganda. You
can already find on the internet deep fake of presi-
dents”. Protecting information in the information war
is essential, and AI can make a change at individual
levels: ”privacy issues, surveillance”, ”ethical con-
cerns regarding personal information and integrity”
Student Views in AI Ethics and Social Impact
31

or at mass levels: ,”It could get too much access to
important features”, it could ”leak dangerous infor-
mation”, and provide a ”better control and influences
over masses”.
There is a threat that AI could become uncon-
trolled, 12.50% men and 9.59% women expressed
this concern; they mention ”Self-improvement of al-
gorithms, to the point where control is lost”, and that
AI could ”become self-conscious”.
The last concern mentioned by students is not re-
lated to ethics but more to loss of human abilities
10.16% men and 15.07% women referred to it: ’De-
crease in the level of intelligence of humanity”,” We
can become dependent on it and lose our ability to
think”.
Conclusion. Men and women perceive potential
threats relatively in the same manner; even if the men
are more aware of the destructive character, they men-
tion war, AI controlled drones, terrain recognition, or
informational war in terms of sensitive information
leakage. In addition, men appear to be more aware of
the phenomenon of fake news and disinformation.
4.3 Q3: Who Is Willing to Sacrifice
Ethical Values for Money and Social
Status? Is There a Difference
Between How Women and Men
Perceive Them?
When people are asked about themselves, they often
respond how they would like to be or act (Rogers,
1966), so instead of asking if they would give up
financial benefits and status for ethical reasons, we
asked them to provide reasons for which they would
follow or not a career path in AI. If the number of
students who did not answer the previous questions
was relatively small, in these cases a larger number
of students had chosen not to answer or had clearly
specified that they did not know. When asked to give
reasons for not working in AI, 21.92% women and
34.11% men did not give any reason, and 17.81%
women and 6.98% stated that they want another ca-
reer. When asked for reasons to work in AI, 28.77%
women and 35.16% men did not provide reasons,
since the other 15.07% women and 9.38% men clearly
specified that they do not want a career in AI.
We grouped the items in the responses into two
large categories: ethical and non-ethical reasons. The
non-ethical reasons can be classified into financial and
non-financial reasons, summarizing more than 45%
for both men and women for both questions. In Table
5 we can see the key items for these non-financial per-
sonal reasons for choosing or not choosing to work in
an AI project for men versus women.
The non-ethical reasons are mostly financial - the
number of jobs and the opportunities to work in AI,
the pay for such a job. Men are more interested in
these aspects 18.75% compared to 12.33% women:
”Current lack of AI-focused jobs that allow transi-
tioning between workplaces for non-seniors.”, ”Hard
to get it”,”it has a very right chance that it will not
pay well because you gotta be on top”.
Ethical reasons are present in 13.70% of the
women’s responses and 10.08% in the men’s re-
sponses. Some perceive that AI is heading in a bad
direction 6.85% women versus 3.88% men, and some
believe that there are moral problems 4.11% women
versus 3.88% men. The students who clearly specify
that they do not want to contribute to AI’s develop-
ment due to ethical reasons or because the fact job
cuts and the effect on the people are 2.72% women
and 2.33% men.
Results Discussion.
There are no significant differences between gen-
ders in students’ perceptions related to beliefs and
perceptions, although there are some minor differ-
ences between men and women regarding the mention
of more frequent war fair, inaccurate information, fi-
nancial aspects, and ethical reasons.
Both men and women mentioned that the domains
most affected by AI development are the domains that
are already using AI (medicine, art, military, automa-
tion). A notable difference between men and women
appears only in fake news propagation and disinfor-
mation, aspects most mentioned by men. Women
mention loss of human abilities reasons more than
men. When asked about the reasons for a career in
AI, women more mentioned the desire to help: 9.59%
vs 6.25%: ”I could save people from a rare disease”,
”To help people with this technology in each aspect
of life to make a lot of comfort in society and help the
planet be more healthy”. Another difference is the
fact that men are more preoccupied by the financial
aspects (number of job opportunities and money) and
they are less preoccupied about ethics, compared to
women, who are more preoccupied about ethics, they
want more ”to help”.
5 THREATS TO VALIDITY
Through the analysis and application of the commu-
nity standards outlined in (Ralph, 2021), our objec-
tive was to minimize any possible risks. Similar prac-
tices have also been applied in other research pa-
pers, as in (Petrescu et al., 2023; Kiger and Varpio,
2020). We also mitigated the potential threats to va-
CSEDU 2025 - 17th International Conference on Computer Supported Education
32

Table 5: Key item classes percentages for personal non-financial reasons of men versus women.
Personal reasons for not choosing Men — Women
Complex/Difficult 13.95% — 12.33%
Math 13.95% — 10.96%
Boring 5.43% — 1.33%
Constantly evolving 0.00% — 4.11%
No interest 6.20% — 8.22%
Other career 6.98% — 17.81%
Personal reasons for choosing Men — Women
Interesting 30.47% — 31.51%
Great impact 5.47% — 6.85%
AI is the future 9.38% — 9.59%
lidity that were identified in software engineering re-
search (Ralph, 2021). Due to the guidelines consid-
ered, we have identified and analyzed three aspects:
construct validity, internal validity, and external va-
lidity. For internal validity, we focus specifically on
the participant set, participant selection, dropout con-
tingency measures, and author biases.
Construct Validity. To reduce the authors’ biases,
the questions were developed in a multi-step process
as outlined in the Survey Design. The suggested sur-
vey questions aligned with the research objectives as
stated in the Introduction.
Internal Validity. The potential internal threats iden-
tified by us were participant and participant selection,
drop-out rates, author subjectivity, and ethics.
Participant Set and Participant Selection. Ev-
ery student enrolled in the AI course, regardless
of gender or other characteristics, has been noti-
fied about the survey and asked to participate in it.
Consequently, the target group of participants was
comprehensive, eliminating any potential risks as-
sociated with the set of participants or their selec-
tion.
Drop-Outs Rates. Due to the voluntary nature
of the survey, we had limited methods to reduce
the dropout rates. The survey consisted of only a
few questions in order to increase student partici-
pation. The open-ended questions were optional.
By outlining the benefits of our research and al-
lowing the survey to remain open for two weeks,
we encouraged participation.
Author Subjectivity. Aware of the possible sub-
jectivity in data processing, we have taken this
into account and examined it. We tried to re-
duce this risk by using text analysis according
to the recommended data processing protocols.
Additionally, by taking into account suggested
data processing practices, we have validated each
other’s work. By debating every facet (includ-
ing the clarity of the approach, the selection of
keywords, and the themes), we attempted a non-
subjective approach.
Ethics in our Research. We demonstrated our
commitment to ethics by providing participants
with information about our purpose of collecting
data, our anonymous data collection method, and
our intended use of the data. Additionally, we
made it clear that participation was voluntary, and
some of them chose not to do so as evidence (from
230 students, only 198 participated).
External Validity. We examine the potential to gen-
eralize the findings of our study. One concern is
whether the results can be extrapolated to a broader
cohort of AI (or even IT) students. We mention that
any extrapolation can not be made to the whole so-
ciety since we considered a specific cohort. How-
ever, we can extrapolate to the students’ set enrolled
in IT, with some caution, because the percentage of
women who participated in the study is comparable
to the percentage of women enrolled in general in
Computer Science studies in universities. Also, the
men/women percentages in our study correlate with
the global gender percentages in STEM according to
the Global Gender Gap Report (2023) (Zahidi, 2023).
6 CONCLUSION AND FUTURE
WORK
This research aims to evaluate students’ attitudes and
interest in AI advancement and ethics with respect to
gender identity. The survey design was iterative, with
changes made to ensure accuracy. The data were col-
lected in English to minimize translation threats and
ensure accurate analysis.
The study involved 230 second-year computer sci-
ence students, of which 198 participated. The study’s
female representation was statistically significant, as
it correlates with the gender percentages of women
enrolled in computer science in universities. The
Student Views in AI Ethics and Social Impact
33

methodology used was thematic analysis, with key
items divided into classes based on frequency.
The responses to the survey revealed that AI will
significantly impact daily life, particularly in areas
such as medicine, education, and photo/video pro-
cessing. While there are no significant differences
between men and women in their perceptions of the
most affected domains, men are more aware of poten-
tial changes in Computer Science, autonomous driv-
ing, image and video processing, and chatbot usage.
Women, however, mention more the impact of losing
human abilities.
Both men and women perceive potential threats in
the same manner, with men more aware of war, AI
controlled drones, terrain recognition, and informa-
tional war. They also seem to be more aware of fake
news and disinformation.
Ethical reasons were more prominent among
women, with women expressing a desire to help peo-
ple in various aspects of life, such as saving people
from rare diseases and making society more comfort-
able. Men were more preoccupied with financial as-
pects, while women were more concerned with help-
ing others.
The study aimed to minimize potential risks and
validate the findings through construct validity, inter-
nal validity, and external validity.
The study revealed that both men and women had
distinct motivations and priorities when it came to
emerging technologies. While men focused more
on financial gains and advancements, women had
a stronger inclination towards ethical considerations
and helping others. Overall, the research shed light on
the gender differences in motivations and highlighted
the need for a balanced approach in the development
and implementation of emerging technologies.
In the future, we hope to expand the study to in-
clude a larger and more diverse cohort of students
from other universities, resulting in a widely compa-
rable data set across European or international institu-
tions. Analyzing the data not only in terms of gender
but also comparing differences among different na-
tionalities as well as the development over time for
several generations of students would be great next
steps, and the paper at hand is the first step in that
direction.
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